diff --git a/notebooks/ePix100/Correction_ePix100_NBC.ipynb b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
index b904c578242b67c7e9918fdec8f66c7dd19d4ded..03b9bbded634fbf2c8ff1f18914ac062c8cf07af 100644
--- a/notebooks/ePix100/Correction_ePix100_NBC.ipynb
+++ b/notebooks/ePix100/Correction_ePix100_NBC.ipynb
@@ -8,7 +8,14 @@
"\n",
"Author: European XFEL Detector Group, Version: 2.0\n",
"\n",
- "The following notebook provides data correction of images acquired with the ePix100 detector."
+ "The following notebook provides data correction of images acquired with the ePix100 detector. \n",
+ "\n",
+ "The sequence of correction applied are:\n",
+ "Offset --> Common Mode Noise --> Relative Gain --> Charge Sharing --> Absolute Gain.\n",
+ "\n",
+ "Offset, common mode and gain corrected data is saved to /data/image/pixels in the CORR files.\n",
+ "\n",
+ "If pattern classification is applied (charge sharing correction), this data will be saved to /data/image/pixels_classified, while the corresponding patterns will be saved to /data/image/patterns in the CORR files."
]
},
{
@@ -17,15 +24,15 @@
"metadata": {},
"outputs": [],
"source": [
- "in_folder = \"/gpfs/exfel/exp/CALLAB/202031/p900113/raw\" # input folder, required\n",
- "out_folder = \"/gpfs/exfel/data/scratch/ahmedk/test/remove/epix_correct\" # output folder, required\n",
+ "in_folder = \"/gpfs/exfel/exp/HED/202202/p003121/raw\" # input folder, required\n",
+ "out_folder = \"\" # output folder, required\n",
"metadata_folder = \"\" # Directory containing calibration_metadata.yml when run by xfel-calibrate\n",
"sequences = [-1] # sequences to correct, set to -1 for all, range allowed\n",
"sequences_per_node = 1 # number of sequence files per cluster node if run as slurm job, set to 0 to not run SLURM parallel\n",
- "run = 9988 # which run to read data from, required\n",
+ "run = 156 # which run to read data from, required\n",
"\n",
"# Parameters for accessing the raw data.\n",
- "karabo_id = \"MID_EXP_EPIX-1\" # karabo karabo_id\n",
+ "karabo_id = \"HED_IA1_EPX100-1\" # karabo karabo_id\n",
"karabo_da = \"EPIX01\" # data aggregators\n",
"db_module = \"\" # module id in the database\n",
"receiver_template = \"RECEIVER\" # detector receiver template for accessing raw data files\n",
@@ -48,7 +55,7 @@
"in_vacuum = False # detector operated in vacuum\n",
"integration_time = -1 # Detector integration time, Default value -1 to use the value from the slow data.\n",
"fix_temperature = -1 # fixed temperature value in Kelvin, Default value -1 to use the value from files.\n",
- "gain_photon_energy = 9.0 # Photon energy used for gain calibration\n",
+ "gain_photon_energy = 8.048 # Photon energy used for gain calibration\n",
"photon_energy = 0. # Photon energy to calibrate in number of photons, 0 for calibration in keV\n",
"\n",
"# Flags to select type of applied corrections.\n",
@@ -360,7 +367,6 @@
" blockSize=blockSize\n",
")\n",
"\n",
- "\n",
"# *****************Histogram Calculators****************** #\n",
"histCalCor = xcal.HistogramCalculator(\n",
" sensorSize,\n",
@@ -456,7 +462,7 @@
" nCells=memoryCells,\n",
" blockSize=blockSize\n",
" )\n",
- " \n",
+ "\n",
" if absolute_gain:\n",
" histCalAbsGainCor = xcal.HistogramCalculator(\n",
" sensorSize,\n",
@@ -487,7 +493,7 @@
" blockSize=[x, y],\n",
" parallel=run_parallel,\n",
" )\n",
- " histCalSECor = xcal.HistogramCalculator(\n",
+ " histCalCSCor = xcal.HistogramCalculator(\n",
" sensorSize,\n",
" bins=nbins,\n",
" range=hrange,\n",
@@ -495,6 +501,14 @@
" nCells=memoryCells,\n",
" blockSize=blockSize,\n",
" )\n",
+ " histCalGainCorClusters = xcal.HistogramCalculator(\n",
+ " sensorSize,\n",
+ " bins=nbins,\n",
+ " range=hrange*hscale,\n",
+ " parallel=run_parallel,\n",
+ " nCells=memoryCells,\n",
+ " blockSize=blockSize\n",
+ " )\n",
" histCalGainCorSingles = xcal.HistogramCalculator(\n",
" sensorSize,\n",
" bins=nbins,\n",
@@ -543,8 +557,6 @@
" d = gainCorrection.correct(d)\n",
" histCalRelGainCor.fill(d)\n",
"\n",
- " data[index, ...] = np.squeeze(d)\n",
- "\n",
" \"\"\"The gain correction is currently applying\n",
" an absolute correction (not a relative correction\n",
" as the implied by the name);\n",
@@ -561,16 +573,12 @@
" if pattern_classification:\n",
"\n",
" d_clu, patterns = patternClassifier.classify(d)\n",
- "\n",
" d_clu[d_clu < (split_evt_primary_threshold*const_data[\"Noise\"])] = 0\n",
- "\n",
- " data_patterns[index, ...] = np.squeeze(patterns)\n",
- "\n",
+ " \n",
" data_clu[index, ...] = np.squeeze(d_clu)\n",
+ " data_patterns[index, ...] = np.squeeze(patterns)\n",
"\n",
- " d_clu[patterns != 100] = np.nan\n",
- "\n",
- " histCalSECor.fill(d_clu)\n",
+ " histCalCSCor.fill(d_clu)\n",
"\n",
" # absolute gain correction\n",
" # changes data from ADU to keV (or n. of photons)\n",
@@ -584,13 +592,19 @@
" if pattern_classification:\n",
" # Modify pattern classification.\n",
" d_clu = d_clu * gain_cnst\n",
+ " \n",
" if photon_energy > 0:\n",
" d_clu /= photon_energy\n",
"\n",
- " histCalGainCorSingles.fill(d_clu)\n",
- "\n",
" data_clu[index, ...] = np.squeeze(d_clu)\n",
"\n",
+ " histCalGainCorClusters.fill(d_clu)\n",
+ " \n",
+ " d_sing = d_clu[patterns==100] # pattern 100 corresponds to single photons events\n",
+ " if len(d_sing):\n",
+ " histCalGainCorSingles.fill(d_sing)\n",
+ "\n",
+ " data[index, ...] = np.squeeze(d)\n",
" histCalCor.fill(d)"
]
},
@@ -609,7 +623,9 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {},
+ "metadata": {
+ "scrolled": false
+ },
"outputs": [],
"source": [
"for f in seq_files:\n",
@@ -668,14 +684,14 @@
" dtype=np.float32)\n",
"\n",
" if pattern_classification:\n",
- " # Save /data/image//pixels_classified in corrected file.\n",
+ " # Save /data/image/pixels_classified in corrected file.\n",
" datasetc = ofile.create_dataset(\n",
" f\"{data_path}/pixels_classified\",\n",
" data=data_clu,\n",
" chunks=dataset_chunk,\n",
" dtype=np.float32)\n",
"\n",
- " # Save /data/image//patterns in corrected file.\n",
+ " # Save /data/image/patterns in corrected file.\n",
" datasetp = ofile.create_dataset(\n",
" f\"{data_path}/patterns\",\n",
" data=data_patterns,\n",
@@ -739,9 +755,8 @@
" 'label': 'Relative gain corr.'\n",
" })\n",
"\n",
- "\n",
"if pattern_classification:\n",
- " ho, eo, co, so = histCalSECor.get()\n",
+ " ho, eo, co, so = histCalCSCor.get()\n",
" d.append({\n",
" 'x': co,\n",
" 'y': ho,\n",
@@ -749,7 +764,7 @@
" 'drawstyle': 'steps-mid',\n",
" 'errorstyle': 'bars',\n",
" 'errorcoarsing': 2,\n",
- " 'label': 'Isolated photons (singles)'\n",
+ " 'label': 'Charge sharing corr.'\n",
" })\n",
"\n",
"fig = xana.simplePlot(\n",
@@ -782,6 +797,17 @@
" })\n",
"\n",
" if pattern_classification:\n",
+ " ho, eo, co, so = histCalGainCorClusters.get()\n",
+ " d.append({\n",
+ " 'x': co,\n",
+ " 'y': ho,\n",
+ " 'y_err': np.sqrt(ho[:]),\n",
+ " 'drawstyle': 'steps-mid',\n",
+ " 'errorstyle': 'bars',\n",
+ " 'errorcoarsing': 2,\n",
+ " 'label': 'Charge sharing corr.'\n",
+ " })\n",
+ " \n",
" ho, eo, co, so = histCalGainCorSingles.get()\n",
" d.append({\n",
" 'x': co,\n",
@@ -792,7 +818,7 @@
" 'errorcoarsing': 2,\n",
" 'label': 'Isolated photons (singles)'\n",
" })\n",
- "\n",
+ " \n",
" fig = xana.simplePlot(\n",
" d, aspect=1, x_label=f'Energy ({plot_unit})',\n",
" y_label='Number of occurrences', figsize='2col',\n",
@@ -855,9 +881,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "cal_venv",
"language": "python",
- "name": "python3"
+ "name": "cal_venv"
},
"language_info": {
"codemirror_mode": {